Assessing the validity of inertial measurement units for shoulder kinematics using a commercial sensor‐software system: A validation study

Abstract Background and  Aims Wearable inertial sensors may offer additional kinematic parameters of the shoulder compared to traditional instruments such as goniometers when elaborate and time‐consuming data processing procedures are undertaken. However, in clinical practice simple‐real time motion analysis is required to improve clinical reasoning. Therefore, the aim was to assess the criterion validity between a portable “off‐the‐shelf” sensor‐software system (IMU) and optical motion (Mocap) for measuring kinematic parameters during active shoulder movements. Methods 24 healthy participants (9 female, 15 male, age 29 ± 4 years, height 177 ± 11 cm, weight 73 ± 14 kg) were included. Range of motion (ROM), total range of motion (TROM), peak and mean angular velocity of both systems were assessed during simple (abduction/adduction, horizontal flexion/horizontal extension, vertical flexion/extension, and external/internal rotation) and complex shoulder movements. Criterion validity was determined using intraclass‐correlation coefficients (ICC), root mean square error (RMSE) and Bland and Altmann analysis (bias; upper and lower limits of agreement). Results ROM and TROM analysis revealed inconsistent validity during simple (ICC: 0.040−0.733, RMSE: 9.7°−20.3°, bias: 1.2°−50.7°) and insufficient agreement during complex shoulder movements (ICC: 0.104−0.453, RMSE: 10.1°−23.3°, bias: 1.0°−55.9°). Peak angular velocity (ICC: 0.202−0.865, RMSE: 14.6°/s−26.7°/s, bias: 10.2°/s−29.9°/s) and mean angular velocity (ICC: 0.019‐0.786, RMSE:6.1°/s−34.2°/s, bias: 1.6°/s−27.8°/s) were inconsistent. Conclusions The “off‐the‐shelf” sensor‐software system showed overall insufficient agreement with the gold standard. Further development of commercial IMU‐software‐solutions may increase measurement accuracy and permit their integration into everyday clinical practice.


| INTRODUCTION
The assessment of upper limb function has become a viable tool in clinical decision making for professions in the medical and athletic field. In overhead sports, the monitoring of shoulder range of motion (ROM) has been emphasized, to distinguish between physiological adaptation and maladaptation. 1,2 Due to the excessive forces and repetitive loading of the shoulder complex, limited internal rotation (IR) and increased external rotation (ER) ROM was observed when comparing dominant and nondominant shoulders of baseball players in 90°abduction (ABD). 3 Similar side-to side differences were evident during shoulder vertical flexion (VFLEX), horizontal flexion (HFLEX) and total range of rotational motion, which is defined as the total arc of ER and IR. [3][4][5] These adaptations may serve clinicians and trainers as potential predictors for future injuries in overhead athletes. [6][7][8][9] Additional kinematic parameters such as angular velocities may be utilized as indicators for movement smoothness in clinical rehabilitation. [10][11][12][13] Traditional instruments to evaluate shoulder kinematics include digital or analog goniometers as well as gravity-based inclinometers.
However, goniometers are prone to in accuracy, since the obtained angles rely on the type of joint and movement, investigators experience, and patient positioning. [14][15][16] Furthermore, they are limited to static measurement conditions while only delivering ROM output without additional kinematic parameters.
On the other hand, camera-based motion capture (Mocap) is still referenced as the gold standard for kinematic assessments of upper limb segments due to its high accuracy. [17][18][19][20][21][22] Yet, these systems may not be suitable for clinical practice since they require a large amount of preparation time, experienced operators, and a laboratory environment to achieve valid results. A promising alternative to objectively quantify body kinematics are portable devices such as inertial measurement units (IMU). 23,24 Major advantages of these sensors compared to the gold standard are relative costeffectiveness, reduced time investment and the capability to extract real-time data. IMU sensors are valid instruments which can be applied in different field-applications such as clinical or scientific movement analysis, monitoring of activities of daily living as well as sports performance assessment. [25][26][27] Some specific examples may be the assessment of postural sway, 28 gait analysis, 29- 37 The authors concluded that validity tends to decrease with increasing task complexity. [35][36][37] Although these results indicate sufficient validity, most investigations utilized highly customized software and fusion algorithms as well as adapted calibration methods. There is only limited evidence for well-established technology transfer of IMU systems into the clinical field. 38,39 A primary reason for this is the elaborate and time-consuming data processing procedures required (e.g., sensor set-up and calibration, adaptation of sensor-fusion algorithms, data export), which do not offer simple real-time motion analysis. Therapists, trainers, and other professionals should be able to set-up IMU systems in a time-efficient manner, allowing for accurate data capture and generation of clinically relevant parameters. Therefore, such "off-the-shelf" solutions might be appropriate when quantifying therapy progress or obtaining real time feedback regarding individual movement quality. Without advanced technical knowledge and training of the investigators, the transfer of valid IMU measurement constructs into everyday clinical practice appears challenging.
Accordingly, this study aims to validate a commercial "off-theshelf" IMU sensor-software system for the assessment of active shoulder kinematics during single-and multiplanar movements, compared with three-dimensional camera system. Sufficient criterion validity may be expected for kinematic parameters during singleplane shoulder movements.

| Participants
24 asymptomatic participants (9 female, 15 male, age: 29 ± 4 years, height: 177 ± 11 cm, weight: 73 ± 14 kg) were included based on similar research in this field. [40][41][42] The cohort was recruited from the university's campus and consisted of sedentary and recreationally active adults to imitate a broad population. Inclusion criteria were age of at least 18 years, the absence of any (acute or chronic) shoulder pain, as well as understanding, and signing the provided written informed consent. Ethical approval was given by the university's ethics review board (grant number: 74/2020).

| Instrumentation
Kinematic data from two portable IMU sensors (Wave Track inertial system; Cometa Systems) were compared against a 10-camera

| Preparations and testing procedure
Two portable IMU sensors were attached in a standardized way to the participants sternum (10 cm below the jugular fossa) and the frontal upper arm (15 cm below the acromion process) using rigid tape ( Figure 1). Manufacturer guidelines only provided the broad requirements on placing the sensors on the upper arm and the sternum without more specific recommendations (User manual-EMG and Motion Tools v. 6.0). In addition, 23 retroreflective markers were attached to the participants torso and both arms with double layered tape based on the "upper limb model". 21 The participants were placed on a chair to minimize evasive movements of the trunk during the measurements. Before data assessment, the participants were asked to remain seated motionless in the neutral, anatomical position (trunk upright, arms, and hands lateral at the trunk) for calibration of the mocap system. Afterwards the "T-pose" (arms 90°a bducted) calibration pose for the IMU sensors was repeated before each measurement to align the sensor axes with the anatomical axis ( Figure 1). Subsequently, single-and multiplanar movements were performed (movement characteristics are summarized in Table 1

| Data processing and data reduction
Kinematic IMU data was collected and processed using an "off-the- Arm elevation to the front (elbow extension).
Vertical extension VEXT Sagittal Arm relaxed next to the trunk (elbow extended).
Elevating the arm backwards (elbow extension).
External rotation ER Transverse Arm relaxed lateral at the trunk (90°elbow flexion).
Rotation of the lower arm outwards while maintaining it parallel to the floor (90°elbow flexion).
Internal rotation IR Transverse Arm relaxed lateral at the trunk (90°elbow flexion).
Moving the lower arm inwards while maintaining it parallel to the floor (90°elbow flexion).
Complex arm pattern interpolating IMU data. This step was necessary to match the extracted outcome data sheets from both systems.

| RESULTS
All participants were able to execute the movement tasks. Interpretation of ICC indicators were based on those used in previous investigations: poor (less than 0.5), moderate (between 0.5 and 0.7), good (between 0.7 and 0.9) and excellent (greater than 0.9). 36 Bland and Altman analysis for the kinematic measures are summarized in Tables 2 and 3  between the gold standard and the IMU "off-the-shelf" system.
Visual analysis of the Bland and Altmann plots revealed homoscedastic distribution of ROM data (see supporting Information: File 2).

| DISCUSSION
Portable sensor systems have become a growing field of interest, for professionals performing clinical assessments in multiple rehabilitative settings. 12,29,43 This study aimed to validate a commercially available "off-the-shelf" IMU sensor-software system for the assessment of shoulder kinematics during single-and multiplanar movements. Although IMU systems generally may exhibit sufficient validity for shoulder kinematics, there is considerable variability between studies due to the large amount of customization of software and calibration methods. [44][45][46] The findings of the present experiment indicate that the investigated "off-the-shelf" sensor system achieved the highest but still insuffi- Therefore, the used commercial "off-the-shelf" sensor-software system may not be mature enough for application in overhead athletes or the general clinical population. Although time-efficiency was not assessed in this trial, it took a relatively short time (less than 10 min) to prepare each subject, calibrate the IMU system and evaluate all movement conditions. Additionally, the software interface and set-up procedure were straight forward and easy to use. If the measurement errors were eliminated, the system could allow health professionals to assess multiple dynamic tests without interruptions, whilst gaining and extracting real-time movement data in the future.

| Limitations
A prevalent source of error in body-worn sensor systems lies in soft tissue artifacts, especially in the frontal and sagittal planes. 48 Errors due to muscle tension, sensor tilt and rotation were noticeable in this investigation, which may have led to biased data peaks or the inability to recognize motion. This problem may be solved by attaching the sensors on bony landmarks at the elbow or by using skin-tight circular straps like those used in a similar investigation. 36 However, the exact sensor positioning was not provided accurately enough in the software. In addition, magnetic disturbances within the measurement volume may have limited the precision of the used IMU sensorsoftware system. Although calibration procedures included the magnetometers, sudden changes in the laboratory environment may not be omitted. This may also explain the relatively large amount of corrupted motion outputs in several trials. Following the calibration procedures, it was not possible to track whether the alignment of the sensors axis and the anatomical axis was successful, which potentially biased all outcomes. In this regard, repeated measurements for reliability assessment may have delivered viable information on time-and measurement dependent sources of error.
Overall, the greatest limitation of commercially available systems is that the integrated sensor fusion as well as output software cannot be edited by the investigator. This makes it generally easier to handle for clinicians but prohibits further interventions concerning individualization or task specifications.

| CONCLUSIONS
Overall, limited agreement was evident between the portable "offthe-shelf" sensor-software system and the gold standard (Mocap).
Although the overall criterion validity may not be sufficient yet, it is important to understand that commercially available and applicable automatic processing software might be particularly important for the professionalization of therapy and training practices. Further research is necessary to investigate whether modified "off-the-shelf" mobile sensor-software systems are accurate enough to assess clinically important adaptations in shoulder kinematics for athletic and clinical populations.